A traditional data center conventionally includes a plurality of individual computing resources. A data center (or other physical space) beneficially has, where possible, an optimized heating and cooling infrastructure. Maintaining data centers at desired temperatures (e.g., set points) helps prevent computer hardware (e.g., information technology (IT) infrastructure) from overheating and malfunctioning. To this end, many data centers are maintained or cooled to relatively low temperatures (e.g., 65° F.) to increase equipment reliability and useful life, and to avoid downtime for repair and/or replacement.
Moreover, data center temperatures are routinely changing, depending on which IT equipment is running at any given time. For example, some IT infrastructure of the data center may run during the day, while other IT infrastructure of the data center may operate at night. To accommodate such moving hot spot targets, existing systems resort to a sort of ‘overkill’ by cooling the entire volume of the data center to well below the set point and/or cooling the data center at all times, which increases operational costs. Moreover, with the increasing awareness and desire to operate in a green manner, such excessive use of energy is undesirable.
Additionally, in data centers the air heating and cooling infrastructure, e.g., a heating, ventilation, and air conditioning (HVAC) system, may commence operation upon sensing a change in temperature in the data center. That is, instead of the HVAC system running constantly, the HVAC system may be run intermittently. For example, the temperature of the data center may rise due to the operation of IT infrastructure in the data center. The rise in temperature may be detected, e.g., by a thermostat, and the HVAC system may commence operation to produce cooling air to react to the rising data center temperature. However, reactively addressing the resultant rise in data center temperatures may be inefficient and may increase data center costs.
Moreover, energy costs may fluctuate, e.g., throughout a given day, throughout a season, throughout a year, etc., based on, for example, energy demand. For example, energy demand (and energy costs) may be lower during the middle of the night. Conversely, energy demand (and energy costs) may be highest during the middle of the day. However, conventional data centers, which provide a constant data center temperature or provide reactionary cooling (i.e., cooling in response to a detected rise in data center temperature), do not account for and/or leverage current energy costs. Thus, conventional data center air systems may be inefficient and may increase data center costs.
Accordingly, there exists a need in the art to overcome the deficiencies and limitations described hereinabove.